Abstract Submission Deadline: January 29, 2020
4-Page Submission Deadline: May 1, 2020 - you can submit now through your Papercept login. 
4-Page Decision Notifications: June 10, 2020
Final Workshop Paper Deadline: June 18, 2020 July 11, 2020 (for delayed notifications)

High-quality 1-page abstracts are requested in the following topical areas for contributed presentations. These 1-page abstracts will appear in the conference program. Following the acceptance of the workshop abstracts, the authors will have the option to submit a full paper (4 pages). Accepted 4-page papers will be published in the workshop proceedings published by IEEE and included in IEEE Xplore.

Use these templates for your 4-page submission.

Codes are provided below to indicate which Workshop you will be submitting to.



1: Deep Learning for Biomedical Image Reconstruction

The Workshop on Deep Learning for Biomedical Image Reconstruction will be held as part of the 2020 IEEE International Symposium on Biomedical Imaging (ISBI). Machine learning has recently received a large amount of interest for the reconstruction of biomedical and pre-clinical imaging datasets. This workshop focuses on the recent developments and challenges in machine learning for image reconstruction, and its focus is on original work aimed to develop new state-of-the-art techniques and their biomedical imaging applications.

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2: Computational Optical Imaging

This workshop will encompass all aspects of computational imaging for optical bioimaging and microscopy. Computational imaging is distinguished from image processing by the role of computation in the image formation process and encompasses the goal of joint design of imaging system hardware and software. New computational imaging methods for biological optical microscopy enable super-resolution, 3D reconstruction, phase imaging, and digital aberration correction. Speakers will be chosen across a broad spread of biological applications from basic microscopy to neuroscience and stem cell biology.

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3: Interaction of Geometry and Topology in Biomedical Imaging

Geometric approaches have been very effective in quantifying and characterizing complex anatomical shape differences and changes in biomedical images. In image segmentation, various topological approaches such as level sets, graph cuts and fuzzy connectedness have been effective. However, it's very difficult to separate topology from geometry in images. Often the combinations of geometric and topological approaches are more effective in quantifying complex images. For instance, topological constraints are enforced to have consistent shape preserving image deformation. Theoretically, the Gauss-Bonnet theorem connects geometry and topology through a single mathematical equation. Recently, topological data analysis (TDA) has been popular in revealing topological features that are persistent over multiple scales. TDA often employs geometric methods in quantifying topological changes. The main aim of this workshop is to increase the awareness of the interaction between geometrical and topological approaches to the biomedical imaging community. The program will include invited talks, as well as regular oral and poster sessions with contributed research papers. The best paper and poster awards will be given.

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4: Deep Image Analysis and Understanding: from Applications to Products

This workshop will focus on the latest and greatest Deep Learning methodologies for medical image analysis. We will present and discuss the methods that have shown most robustness - over multiple tasks, across multiple institutes; along with the newest models being proposed today. In addition to network development, attention needs to be given to the training and testing methods used, to reach standardized rigorous evaluation. In the afternoon program, we will focus on how to turn an algorithm into a product, and translate it into the clinic. We will have invited speakers who will share their experiences in moving from research to development and entrepreneurship.

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Workshop Chairs

Ge Yang
Punam Kumar Saha